Article Per Year (5 Year)
p-Index From 2020 - 2025
2.144
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Parameter: Journal of Statistics Journal Of Human And Education (JAHE) Journal of Learning Improvement and Lesson Study Journal of Data Insights Prosiding Seminar Nasional Unimus
Alwan Fadlurohman
Universitas Muhammadiyah Semarang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Generalized Space Time Autoregressive Modeling With Variable Exogenous (Gstar-X) (Case Study: Inflation In Six Cities Of Central Java) Alwan Fadlurohman; Tiani Wahyu Utami; Rochdi Wasono
Prosiding Seminar Nasional Unimus Vol 3 (2020): Optimalisasi Hasil Penelitian dan Pengabdian Masyarakat Menuju Kemandirian di Tengah P
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Inflasi adalah kecenderungan naiknya harga barang dan jasa yang berlangsung secara terus menerus. Inflasi merupakan data time series bulanan yang diduga juga dipengaruhi oleh unsur antar lokasi. Pemodelan untuk peramalan inflasi yang melibatkan unsur waktu dan lokasi (spatio temporal) dapat menggunakan metode Generalized Space Time Autoregressive (GSTAR). Untuk menambah akurasi dalam peramalan, model GSTAR dikembangkan menjadi model GSTARX dengan melibatkan variabel eksogen. Variabel eksogen yang digunakan dalam pemodelan GSTARX untuk peramalan Inflasi ini adalah variasi kalender idul fitri yaitu inflasi pada bulan di hari raya idul fitri. Studi kasus dalam pemodelan GSTARX ini diterapkan untuk peramalan inflasi enam kota Survei Biaya Hidup (SBH) di Jawa Tengah yaitu Cilacap, Purwokerto, Semarang, Kudus, Magelang dan Surakarta. Tujuan penelitian ini adalah ingin mendapatkan model GSTARX yang terbaik untuk pemodelan inflasi enam kota SBH diJawa Tengah. Didapatkan 2 (dua) model GSTARX dengan nilai RMSE masing-masing adalah model dengan bobot lokasi seragam memiliki nilai RMSE sebesar 0,6108, model dengan bobot lokasi invers jarak memiliki nilai RMSE sebesar 0,6124. Dapat disimpulkan bahwa model GSTARX menggunakan bobot lokasi seragam adalah model terbaik. Kata Kunci : GSTAR, GSTARX, Inflasi, Jawa Tengah, Survei Biaya Hidup.
Pengelompokkan Provinsi di Indonesia Berdasarkan Indikator Perumahan dan Kesehatan Lingkungan Menggunakan Metode KMedoids Alwan Fadlurohman; Indah Manfaati Nur
Prosiding Seminar Nasional Unimus Vol 6 (2023): Membangun Tatanan Sosial di Era Revolusi Industri 4.0 dalam Menunjang Pencapaian Susta
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indikator perumahan dan kesehatan lingkungan merupakan salah satu indikator yang sangat penting dalamupaya mewujudkan tujuan dari Tujuan Pembangunan Berkelanjutan (TPB). Kondisi perumahan dankesehatan lingkungan di setiap provinsi di Indonesia berbeda-beda, oleh karena itu dalam melakukan prioritaspeningkatan masalah perumahan dan kesehatan lingkungan juga berbeda. Tujuan atas penelitian ini guna mengklasifikan provinsi di Indonesia atas dasar indikator perumahan dan kesehatan lingkungan untukmengetahui tinggi rendahnya kualitas perumahan dan lingkungan di setiap provinsi. Sehingga, hasil penelitiandengan harap mampu membantu pemerintah mengoptimalkan upaya kesehatan lingkungan. Pengelompokanprovinsi dilakukan dengan metode K-Medoids yang memiliki kelebihan robust terhadap data yangmengandung pencilan. Ukuran kemiripan objek dihitung dengan menggunakan metode jarak Euclidean.Sementara itu, pemilihan jumlah cluster terbaik dilakukan menggunakan indeks silhouette yang menghasilkan2 cluster, dimana pada cluster 1 didapatkan 29 provinsi dengan nilai rata-rata indikator perumahan dankesehatan lingkungan (X2, X4, X5, X6, X7, dan X10) rendah. Lalu cluster 1 didapatkan 5 provinsi dengan nilairata-rata indikator perumahan dan kesehatan lingkungan (X2, X4, X5, X6, X7, dan X10) tinggi. Kata Kunci: Dampak Perkotaan, Pencilan, Pengelompokkan, Sanitasi.
Scientific Article "Lesson Study": Portrait of Improving the Teacher Learning Quality Winaryati, Eny; Iksan, Zanaton Haji; Rauf, Rose Amnah Abd; Sugiharto, Prasetyawan Aji; Fadlurohman, Alwan; Yusrin, Yusrin; Maharani, Endang Tri Wahyuni
Journal of Learning Improvement and Lesson Study Vol. 4 No. 1 (2024): JLILS (June Edition)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jlils.v4i1.87

Abstract

The aim of this research is: photographing the teacher's lesson study learning process, as well as evaluate information findings resulting from LS scientific article preparation activities. This research is based on a phenomenological approach, which focuses more on phenomena that are felt, obtained, responded to, perceived by subjects (humans) towards objects (LS scientific articles) written by teachers. The results of LS teacher's writing of scientific articles, are the result of narrating the implementation of lesson study, producing several findings, namely: firstly, they have writing skills to narrate teaching experiences. Some teachers are doing this skill for the first time. Second, photographing LS activities to narrate in articles, provides an interesting and challenging experience. Third, being able to present learning activities to be published as work results is a source of pride for teachers. Fourth, considering that LS activities are personal experiences, conveying them in written form is like telling a story. Fifth, encourage collaborative writing, as a result of collaborative LS experience, and the results of collaborative work. Sixth, preparing LS articles encourages teachers to look for lots of references, so that good LS articles are produced. Seventh, articles that are prepared collaboratively produce rich ideas, because they create an exchange of ideas to produce better articles. Eighth, there is a correlation between the lesson study stages, the problems that will be solved through learning, and the learning strategies carried out by the teacher. Ninth, LS activities will become teacher best practices in learning, when written in a scientific article. This LS scientific article expands information on the success or best practice of learning for many people. Tenth, the quality of the scientific articles produced is a portrait of the mastery of the lesson study that has been carried out. Suggestion: it needs to be used as a habit for lesson study activities which have an impact on writing scientific articles. The aim is to improve writing skills for teachers, while encouraging collaborative work as a habituation process, as well as training that learning experiences can become best practices that can be disseminated as more useful information.
Integration of GSTAR-X and Uniform location weights methods for forecasting Inflation Survey of Living Costs in Central Java Fadlurrohman, Alwan
Journal of Intelligent Computing & Health Informatics Vol 1, No 1 (2020): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v1i1.5583

Abstract

Inflation is a tendency to increase prices of goods and services that take place continuously. Inflation is a monthly time series data that is thought to be influenced by location elements. Modeling for inflation forecasting that involves time and location (spatio temporal) can use the Generalized Space Time Autoregressive (GSTAR) method. To increase accuracy in modeling and forecasting, the GSTAR model was developed into the GSTARX model by involving exogenous variables. Exogenous Variavel used in GSTARX modeling for forecasting Inflation is a variation of the Eid calendar. This GSTARX modeling is applied for inflation forecasting in six cities Cost of Living Survey (SBH) in Central Java, namely Cilacap, Purwokerto, Semarang, Kudus, Magelang and Surakarta. The purpose of this study is to get the best GSTARX model for inflation forecasting for six SBH cities in Central Java. The selection of the best model from the GSTARX method is seen with the smallest RMSE value of each model. Obtained that the GSTARX model with uniform weights is the best model because it has a smaller RMSE compared to the GSTARX model with inverse distance weights, the RMSE values are 0.6122 and 0.6137, respectively. It can be concluded that the GSTARX method with Uniform weighting can provide better performance and can be used to predict the inflation of the six SBH cities in Central Java in the next 12 periods.
CLASSIFICATION OF MYPERTAMINA APP REVIEWS USING SUPPORT VECTOR MACHINE Fadlurohman, Alwan; Yunanita, Novia; Rohim, Febrian Hikmah Nur; Wardani, Amelia Kusuma; Ningrum, Ariska Fitriyana
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page223-228

Abstract

Indonesia is rich in natural resources, including oil and gas, and it manages these strategic assets through state-owned enterprises, one of which is PT Pertamina. Pertamina is responsible for domestic fuel production, distribution, and price stabilization. To improve efficiency and transparency, Pertamina developed the MyPertamina application that enables cashless fuel purchases, stock monitoring, and up-to-date price information. The application aims to streamline distribution and control fuel prices, thus helping to stabilize the cost of goods and services. MyPertamina also ensures subsidized fuel distribution is more effective and targeted by identifying and verifying subsidy recipients, reducing the potential for abuse. A sentimental analysis of subsidized fuel user reviews using this application is needed to understand the public's views. This research uses the Support Vector Machine (SVM) method to analyze the sentiment of MyPertamina app reviews. This research produced a stable model. Out of 200 reviews, 190 were negative, and nine were positive, with an SVM model accuracy of 97%. Wordcloud visualization shows the words that appear frequently in each sentiment. Positive reviews appreciated the photo verification feature, easy payment, and good service. Negative reviews included verification difficulty, app error, and feature failure.
SOCIAL VULNERABILITY ANALYSIS IN CENTRAL JAVA WITH K-MEDOIDS ALGORITHM Fadlurohman, Alwan; Ayu Nur Roosyidah, Nila; Amalia Annisa, Nafida
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17131

Abstract

To address the limitations of the Social Vulnerability Index (SoVI) in only providing a general overview without pinpointing areas of social vulnerability, a correlational approach paired with a clustering method can be applied. This approach helps in identifying dominant factors and pinpointing socially vulnerable districts or cities in Central Java. The study employs the K-Medoids algorithm, which is advantageous when dealing with outliers in the dataset. Three different distance measures are considered: Euclidean, Manhattan, and Minkowski distances, to identify the optimal clustering of social vulnerability. The evaluation of the best cluster is conducted using the Davies-Bouldin Index, a metric for validating clustering models by averaging the similarity of each cluster to its most similar counterpart. Findings indicate that using the K-Medoids algorithm with Manhattan distance yields the most effective clustering, resulting in two distinct clusters. Cluster 1, comprising 25 districts/cities, is identified as the most vulnerable to natural disasters and challenges in education, demography, economy, and health. Meanwhile, Cluster 2, encompassing 10 districts/cities, includes urban areas with the highest social vulnerability, notably in the proportion of rental housing.
Prediksi Jumlah Penumpang Di Bandara Nasional Ahmad Yani Semarang Menggunakan Holt Winter’s Exponential Smoothing (HWES) Gautama, Rahmad Putra; Fadlurohman, Alwan; Arum, Prizka Rismawati; Dhani, Oktaviana Rahma
Prosiding Seminar Nasional Unimus Vol 7 (2024): Transformasi Teknologi Menuju Indonesia Sehat dan Pencapaian Sustainable Development G
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pesawat terbang memberikan kenyamanan dan kecepatan bagi penggunanya terutama bagi mereka yangmemiliki keterbatasan waktu. Peningkatan jumlah penumpang terus terjadi beberapa bulan ini, sehinggadibutuhkan suatu peramalan dalam mengambil keputusan untuk memprediksi jumlah penumpang gunamemaksimalkan kinerja yang ada. Karena metode Holt Winters Exponential Smoothing tidak sangat akuratdan sesuai dengan asumsi awal dari pola data penelitian, metode ini digunakan. Studi ini bertujuan untukmenggunakan metode Holt Winters Exponential Smoothing untuk meramalkan jumlah penumpang pesawatdi Bandara Nasional Ahmad Yani Semarang. Hasil analisis menunjukkan bahwa metode ini memiliki nilaiMAPE sebesar 13,98%, yang menunjukkan bahwa metode ini adalah pilihan yang baik dan tepat untukmeramalkan jumlah penumpang pesawat di Bandara Nasional Ahmad Yani Semarang. Kata Kunci : Holt Winters Exponential Smoothing, Mape, Penumpang, Peramalan
Data Visualization Excellence: Google Data Studio Workshop At Sekolah Indonesia Kuala Lumpur Amri, Saeful; Fadlurohman , Alwan; Ningrum, Ariska Fitriyana; Purwanto, Dannu; Amri , Ihsan Fathoni; Wardani, Amelia Kusuma; Dhani, Oktaviana Rahma
Journal Of Human And Education (JAHE) Vol. 5 No. 1 (2025): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v5i1.2178

Abstract

The development of information technology and the entry of the industrial revolution 4.0 era has led to the inseparability of human activities related to the use of technology. In today's rapidly growing information age, data is one of the most valuable assets. The ability to collect, analyze, and interpret data is becoming a very important skill not only in the world of work but also in education. Education is the foundation for preparing future generations for increasingly complex global challenges, and a good understanding of data can provide a significant competitive advantage. In schools, the ability to analyze and interpret data is becoming an invaluable skill for students. Along with the development of technology, data visualization has become an effective method to convey information in a more comprehensible manner. In this context, Google Data Studio offers a powerful and easy-to-use tool for creating interactive dashboards that help in analyzing and presenting data. Indonesian Migrant Workers (TKI) are Indonesian citizens who live and work abroad. TKI provide a large contribution of foreign exchange to the country of Indonesia. However, there are problems in the field of education for children whose parents work as TKI in Malaysia, especially education that is relevant to success in terms of opening their own jobs abroad. This is considered important because to get jobs in government agencies or companies in Malaysia, the children of TKI working in Malaysia must compete with job seekers who are Malaysian citizens. One alternative that can be taken to overcome competition in getting jobs is to create your own jobs. Opening your own jobs is not an easy thing. Knowledge and insight about this are needed which are given early on to the children of TKI in school. By teaching Google Data Studio in the form of data visualization to students, they not only learn how to read and interpret graphs and diagrams, but also how to present their own data in a more interesting and informative way. This ability will be very useful in the future, both in academic and professional environments. By providing insight into Google Data Studio to students in schools, these students have the provisions to be able to read data and have the opportunity to work and get decent jobs. As a Community Service activity with an international scope, this activity takes partners in Malaysia, namely the Indonesian School-Kuala Lumpur - SIKL which is located in Sentul, Kuala Lumpur, Federal Territory of Kuala Lumpur. The Community Service Team of Muhammadiyah University of Semarang is very receptive to criticism and suggestions so that the implementation of Community Service in the future can be even better.
FUZZY GEOGRAPHICALLY WEIGHTED CLUSTERING WITH OPTIMIZATION ALGORITHMS FOR SOCIAL VULNERABILITY ANALYSIS IN JAVA ISLAND Fadlurohman, Alwan; Utami, Tiani Wahyu; Amrullah, Setiawan; Roosyidah, Nila Ayu Nur; Dhani, Oktaviana Rahma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1841-1852

Abstract

The Social Vulnerability Index (SoVI) measurement assesses social vulnerability. However, the measurement of SoVI can only describe the general conditions without being able to show which factors dominate. Therefore, a clustering approach has been proposed to characterise the dominant social vulnerability factors. Fuzzy Geographically Weighted Clustering (FGWC) is a method that works for this purpose. FGWC is an extension of the Fuzzy C-Means algorithm, which involves geographical influences in calculating membership values. However, the FGWC method is sensitive because the initial initialisation to determine the centroid is randomised, and it will affect the cluster quality. This research uses a metaheuristic approach to overcome the weakness of FGWC by using Particle Swarm Optimisation (PSO) and Artificial Bee Colony (ABC). This study aims to cluster districts/cities in Java Island using the PSO-FGWC and ABC-FGWC methods based on social vulnerability variables and determine the dominant factors of social vulnerability in each region. Optimum cluster selection uses the index of the largest Partition Coefficient (PC) and the smallest Classification Entropy (CE). Clustering social vulnerability in Java Island resulted in the best clustering using the ABC-FGWC method with 5 optimum clusters based on the PC and CE index values of 0.343 and 1.298, respectively. This research found that social vulnerability exists in each region of Java Island. Cluster 1, consisting of 19 districts/cities, is characterized by vulnerabilities in demography and education. Cluster 2, consisting of 33 districts/cities, is characterized by demographic and health vulnerabilities. Cluster 3, which consists of 24 districts/cities, is dominated by education and economic vulnerability factors. Cluster 4, consisting of 14 districts/cities, has the highest social vulnerability characteristics on the unemployment rate and the proportion of house rent. The last one, cluster 5, consists of 29 districts/cities and has a vulnerability problem in the population growth variable.
MODELING DHF IN CENTRAL JAVA USING HYBRID NONPARAMETRIC SPLINE TRUNCATED-FOURIER SERIES APPROACH Utami, Tiani Wahyu; Maharani, Endang Tri Wahyuni; Fadlurohman, Alwan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1459-1470

Abstract

Regression analysis aims to determine the relationship and influence of predictor variables on response variables through regression curve. The problem with nonparametric regression research so far is that it only uses one approach, causing the estimation results to be biased, even though each data sub-pattern has its own suitability depending on the approach method used. Therefore, the hybrid method emerged as a development of nonparametric regression. Hybrid models are models that combine approach methods, with the hope of increasing accuracy in modeling analysis. This research was carried out using two non-parametric approaches, namely Spline Truncated and Fourier Series. Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus. DHF is endemic and occurs throughout the year, especially during the rainy season because mosquitoes reproduce optimally. The aim of this research is to estimate the Hybrid Nonparametric Spline Truncated -Fourier Series model and apply the estimation results to data on DHF cases in Central Java. The data used to apply the hybrid nonparametric Spline Truncated-Fourier series regression model is DHF in the city/districts of Central Java. Estimation smoothing parameters uses the GCV (Generalized Cross Validation) method. The best model is selected based on largest R-Square and the smallest MSE. Modeling the disease of DHF cases in Central Java using the Spline Truncated-Fourier Series hybrid estimator produced the best model from the Spline Truncated model with two knot points for each predictor and the Fourier Series model with value of 9. Based on the results obtained, it can be compared that the Truncated Spline-Fourier Series hybrid model is better than the Spline Truncated model, this can be seen from the largest R-square, namely 99.94% and the smallest MSE.
Co-Authors Amalia Annisa, Nafida Amalia Putri, Rizka Amri, Saeful Amrullah, Setiawan Anggun Lestari, Febi Ariska Fitriyana Ningrum Aura Hisani, Zahra Ayu Nur Roosyidah, Nila Dannu Purwanto Dhani, Oktaviana Rahma Endang Tri Wahyuni Maharani Eny Winaryati Fauzi, Fatkhurokhman Gautama, Rahmad Putra Ihsan Fathoni Amri Iksan, Zanaton Haji Indah Manfaati Nur Lestari, Febi Anggun M. Al Haris Mughni, Mardiyah Musa, Fitri Diana Prasetyawan Aji Sugiharto Prizka Rismawati Arum Rahma Dhani, Oktaviana Rauf, Rose Amnah Abd Rochdi Wasono Rohim, Febrian Hikmah Nur Roosyidah, Nila Ayu Nur Sarah, Albertus Dion Tiani Wahyu Utami Wardani, Amelia Kusuma Yunanita, Novia Yusrin, Yusrin